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import os
import sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
import numpy as np
import argparse
import h5py
import math
import time
import logging
import matplotlib.pyplot as plt
import torch
torch.backends.cudnn.benchmark=True
torch.manual_seed(0)
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from utilities import get_filename
from models import *
import config
class Transfer_Cnn14(nn.Module):
def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin,
fmax, classes_num, freeze_base):
"""Classifier for a new task using pretrained Cnn14 as a sub module.
"""
super(Transfer_Cnn14, self).__init__()
audioset_classes_num = 527
self.base = Cnn14(sample_rate, window_size, hop_size, mel_bins, fmin,
fmax, audioset_classes_num)
# Transfer to another task layer
self.fc_transfer = nn.Linear(2048, classes_num, bias=True)
if freeze_base:
# Freeze AudioSet pretrained layers
for param in self.base.parameters():
param.requires_grad = False
self.init_weights()
def init_weights(self):
init_layer(self.fc_transfer)
def load_from_pretrain(self, pretrained_checkpoint_path):
checkpoint = torch.load(pretrained_checkpoint_path)
self.base.load_state_dict(checkpoint['model'])
def forward(self, input, mixup_lambda=None):
"""Input: (batch_size, data_length)
"""
output_dict = self.base(input, mixup_lambda)
embedding = output_dict['embedding']
clipwise_output = torch.log_softmax(self.fc_transfer(embedding), dim=-1)
output_dict['clipwise_output'] = clipwise_output
return output_dict
def train(args):
# Arugments & parameters
sample_rate = args.sample_rate
window_size = args.window_size
hop_size = args.hop_size
mel_bins = args.mel_bins
fmin = args.fmin
fmax = args.fmax
model_type = args.model_type
pretrained_checkpoint_path = args.pretrained_checkpoint_path
freeze_base = args.freeze_base
device = 'cuda' if (args.cuda and torch.cuda.is_available()) else 'cpu'
classes_num = config.classes_num
pretrain = True if pretrained_checkpoint_path else False
# Model
Model = eval(model_type)
model = Model(sample_rate, window_size, hop_size, mel_bins, fmin, fmax,
classes_num, freeze_base)
# Load pretrained model
if pretrain:
logging.info('Load pretrained model from {}'.format(pretrained_checkpoint_path))
model.load_from_pretrain(pretrained_checkpoint_path)
# Parallel
print('GPU number: {}'.format(torch.cuda.device_count()))
model = torch.nn.DataParallel(model)
if 'cuda' in device:
model.to(device)
print('Load pretrained model successfully!')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Example of parser. ')
subparsers = parser.add_subparsers(dest='mode')
# Train
parser_train = subparsers.add_parser('train')
parser_train.add_argument('--sample_rate', type=int, required=True)
parser_train.add_argument('--window_size', type=int, required=True)
parser_train.add_argument('--hop_size', type=int, required=True)
parser_train.add_argument('--mel_bins', type=int, required=True)
parser_train.add_argument('--fmin', type=int, required=True)
parser_train.add_argument('--fmax', type=int, required=True)
parser_train.add_argument('--model_type', type=str, required=True)
parser_train.add_argument('--pretrained_checkpoint_path', type=str)
parser_train.add_argument('--freeze_base', action='store_true', default=False)
parser_train.add_argument('--cuda', action='store_true', default=False)
# Parse arguments
args = parser.parse_args()
args.filename = get_filename(__file__)
if args.mode == 'train':
train(args)
else:
raise Exception('Error argument!') |